


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						Optional[Any]        =  logging.get_logger(__name__)

A			:						int        =  {
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''xlm-roberta'''

					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[int]=3_0522 ,   _UpperCAmelCase       :		Union[str, Any]=768 ,   _UpperCAmelCase       :		int=12 ,   _UpperCAmelCase       :		Dict=12 ,   _UpperCAmelCase       :		str=3072 ,   _UpperCAmelCase       :		int="gelu" ,   _UpperCAmelCase       :		Any=0.1 ,   _UpperCAmelCase       :		Dict=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		Optional[int]=2 ,   _UpperCAmelCase       :		int=0.02 ,   _UpperCAmelCase       :		int=1E-1_2 ,   _UpperCAmelCase       :		Optional[Any]=1 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		List[Any]="absolute" ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Tuple=None ,   **_UpperCAmelCase       :		List[Any] ,   )		->  List[Any]:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						use_cache
							lowercase__	     =						classifier_dropout






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




